Open AccessA new function of stereo matching algorithm based on hybrid convolutional neural network
Author(s) -
M. Saad Hamid,
Nurulfajar Abd Manap,
Rostam Affendi Hamzah,
Ahmad Fauzan Kadmin,
Shamsul Fakhar Abd Gani,
Adi Irwan Herman
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i1.pp223-231
Subject(s) - computer science , artificial intelligence , matching (statistics) , convolutional neural network , computation , algorithm , pattern recognition (psychology) , depth map , set (abstract data type) , blossom algorithm , computer vision , mathematics , image (mathematics) , statistics , programming language
This paper proposes a new hybrid method between the learning-based and handcrafted methods for a stereo matching algorithm. The main purpose of the stereo matching algorithm is to produce a disparity map. This map is essential for many applications, including three-dimensional (3D) reconstruction. The raw disparity map computed by a convolutional neural network (CNN) is still prone to errors in the low texture region. The algorithm is set to improve the matching cost computation stage with hybrid CNN-based combined with truncated directional intensity computation. The difference in truncated directional intensity value is employed to decrease radiometric errors. The proposed method’s raw matching cost went through the cost aggregation step using the bilateral filter (BF) to improve accuracy. The winner-take-all (WTA) optimization uses the aggregated cost volume to produce an initial disparity map. Finally, a series of refinement processes enhance the initial disparity map for a more accurate final disparity map. This paper verified the performance of the algorithm using the Middlebury online stereo benchmarking system. The proposed algorithm achieves the objective of generating a more accurate and smooth disparity map with different depths at low texture regions through better matching cost quality.